Radar-Based Rainfall Estimation of Landfalling Tropical Storm “PABUK” 2019 over Southern Thailand

Abstract

Tropical storm PABUK developed from tropical depression first defined on 31 December 2018 in the lower South China Sea. It made landfall in Pak Phanang, Nakhon Si Thammarat province, southern Thailand. PABUK caused heavy rain and flash floods from 3 to 5 January 2019 (D1, D2, and D3) where the total rainfall reached 150–300 mm across 14 provinces of southern Thailand. This paper is aimed to investigate rainstorm properties and rainfall estimation of tropical storm PABUK with weather radar in southern Thailand. The radar data analysis in this study was to extract the radar reflectivity to study rainstorm properties of PABUK over 3 days along southern Thailand derived from the Thunderstorm Identification and Tracking Analysis and Nowcasting (TITAN) algorithm including 5 variables of duration, area, cloud-based height, maximum reflectivity, and speed in the data set. Based on the properties and frequency distribution of 2,557 rainstorms in D1, D2, and D3, rainstorms in D2 and D3 when PABUK made landfall over southern Thailand show a longer lifetime, higher reflectivity, and larger rain-cells as well as it was found efficient in terms of rainfall amount than in D1. In addition, the estimated rainfall using weather radar provides important information of the rainfall distribution for the analysis of the rainstorm as well. These analyses provide a context for interpreting the feasible rainfall estimates based on Z-R relationship during tropical storm PABUK that produced extreme floods in southern Thailand. A Z-R relationship in the form Z = 104R1.5 provided acceptable statistical indicators, making it appropriate for radar estimated rainfall in case studies presented of tropical storm PABUK in southern Thailand. However, the result of this study should be improved to estimate precipitation in case of extremely heavy rainfall in tropical storm occurrence by using radar of southern Thailand and applied for applications of early warning systems.